At present, techniques such as motion detection and facial recognition, which are based on IoT, develop rapidly. However, in practice, there are so many environments and conditions to be distinguished and recognized. For example, in the same environment, there are different types of data such as fire alarm, water level, rainfall, temperature and humidity to be recognized; and when recognizing the same type of data under different environments, persons and objects in the different environments also need to be distinguished. Meanwhile, the application of techniques such as motion detection and facial recognition is somewhat limited by network performance and usage scenarios. For example, motion detection is applied only in detection of moving objects and then triggering of communicative connection. Such motion detection technique cannot meet the demands for intelligently determining many conditions and automatically connecting to different communication objects under different conditions. Moreover, such motion detection does not support some basic functions such as call forwarding busy. So does the facial recognition.